An "Oddity" in Significance Testing Dear Students, In hypothesis testing, the alternative hypothesis is an important "structural component." In fact, it's just as important as the null hypothesis. That's because the researcher will consider Ha to represent the true state of affairs whenever Ho is rejected. Before any data are collected and any conclusion is reached, therefore, the null and alternative hypotheses have "equal standing" within the hypothesis testing procedure. In contrast, there is no alternative hypothesis in significance
testing. The only hypothesis that's involved in this form of inferential
statistics is the null hypothesis. After the data are collected and analyzed,
the researcher does not "turn to Ha"
(if Ho seems implausible) because there
is no alternative hypothesis to which one can turn. Instead, the researcher
simply presents the data-based p-level as an indication of how Paradoxically, the p-value in significance requires the researcher decide whether to conduct a one-tailed or a two-tailed test. And as you learned in Chapter 7, the choice between doing things in a one-tailed or two-tailed manner is made when the researcher sets up Ha to be directional or nondirectional. Thus, significance testing does not have an alternative
hypothesis "on the surface." But there is one lurking in the
background. It's not something that plays a role when conclusions are
reached Sky Huck |

Copyright © 2012 Schuyler W. Huck |
| Book Info | Author Info | Site URL: www.readingstats.com |